用蝙蝠算法优化3D U-Net，提升CT肝肿瘤检测精度
Enhanced Liver Tumor Detection in CT Images Using 3D U-Net and Bat Algorithm for Hyperparameter Optimization
- 结合3D U-Net与蝙蝠算法优化学习率、批量大小等超参
- 在公开数据集上实现高F1分数，低阈值下仍保持良好召回率
- 适合医学影像分析领域，尤其关注漏检防控的临床场景
肝癌是常见且致命的癌症之一，早期检测对治疗至关重要。本文提出一种新型自动化方法，通过将3D U-Net架构与蝙蝠算法结合，优化学习率、批量大小等关键超参数，实现对CT图像中肝肿瘤的精准分割。该方法显著提升了分割的准确性和鲁棒性。在公开数据集上的评估显示，模型在较低预测阈值下仍能保持较高的F1分数，有效平衡了精确率与召回率，对临床诊断中避免漏诊具有重要意义。研究证明，深度学习架构与元启发式优化算法的协同可为复杂分割任务提供高效解决方案。
原文摘要 · Abstract (English)
Liver cancer is one of the most prevalent and lethal forms of cancer, making early detection crucial for effective treatment. This paper introduces a novel approach for automated liver tumor segmentation in computed tomography (CT) images by integrating a 3D U-Net architecture with the Bat Algorithm for hyperparameter optimization. The method enhances segmentation accuracy and robustness by intelligently optimizing key parameters like the learning rate and batch size. Evaluated on a publicly available dataset, our model demonstrates a strong ability to balance precision and recall, with a high F1-score at lower prediction thresholds. This is particularly valuable for clinical diagnostics, where ensuring no potential tumors are missed is paramount. Our work contributes to the field of medical image analysis by demonstrating that the synergy between a robust deep learning architecture and a metaheuristic optimization algorithm can yield a highly effective solution for complex segmentation tasks.
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